With the proliferation of cameras in computing devices such as smartphones, tablets, laptop computers, and the like, it has become increasingly common for such devices to implement face verification systems (also known as face recognition systems) for user authentication. In a conventional face verification system, a device user is typically required to complete an initial enrollment process in which the user is instructed to look squarely at the device's camera, thereby allowing the camera to capture one or more frontal view images of the user's face. The face verification system uses these frontal view image(s) to generate a face template for the user. The face verification system can then compare the face template to images of the user's face that are captured at the point of authentication in order to verify the user's identity.
One problem with the conventional approach above is that, since the enrolled face template relies solely on frontal view images of the user's face, the user must generally look at the device's camera straight-on (i.e., using the same frontal pose used during the enrollment process) at the time he/she wishes to be authenticated. If the user positions his/her face in a manner that is not square with the camera (referred to herein as an “off-pose” position), the authentication is more likely to fail, or at least be delayed until the user turns his/her face into a square position, because the face template does not have any data representing the sides of the user's face. A workaround for this problem is to capture multiple facial poses from the user during the enrollment process by, e.g., instructing the user to look left, right, up, and/or down (in addition to straight-on). However, this workaround makes the enrollment process cumbersome and unnatural for the user. In addition, the captured facial poses may still not reflect the actual poses that the user will present when attempting to authenticate himself/herself, and thus may not result in more accurate or more rapid authentication outcomes.